Abstract For individuals recovering from a stroke, injurious falls often occur due to a stumble, or ?intrinsically generated? trip (i.e., the swinging foot contacts the ground), while walking. A major barrier toward developing effective fall prevention strategies is an inability to determine reliably, in advance, that a walking-related trip will occur. Swing limb motion, however, is dictated by late stance kinematics. Therefore, we propose to develop a novel inference system, based on stance phase kinematics, that can accurately and reliably predict, in real-time, that a trip is about to occur. Thus, if the foot is predicted to strike the ground, the algorithm will inform which steps require intervention in the swing limb's trajectory. Current approaches to fall prevention teach reactive responses to a trip or train individuals post-stroke to minimize the impairments associated with falls (e.g., strength, balance, ROM). Although preventive training can reduce intrinsically generated trips for otherwise healthy older adults, deficits in voluntary muscle activation limits the efficacy of such training in individuals post-stroke. Rather than using a conventional reactive approach, we intend to develop the preliminary tools needed to develop a proactive, integrated, feed-forward controller to inform future engineering approaches (e.g., a multi-channel electrical stimulator, exoskeleton/exosuit) to appropriately intervene in the swing limb's trajectory, only when necessary. The work proposed here is a necessary first step to determine that we can successfully predict trips accurately and with sufficient time to intervene appropriately. To accomplish this goal, we will pursue two Specific Aims. In Specific Aim 1, we will use non-environmental distractors to increase the likelihood of participants experiencing an intrinsically generated trip while walking on both a treadmill, as well as overground. We will then use the recorded limb kinematics to select a feature set for development of a novel inference prediction system. This algorithm will be evaluated offline to determine its accuracy and speed in classifying steps as either trips or non-trips. In Aim 2, we will evaluate the developed inference system in a real-time analysis of trip and non-trip steps during walking. Again, the online system will be evaluated for accuracy and speed during walking trials. At the conclusion of this project, we will have a robust method of detecting an upcoming trip for ?selective? intervention in swing limb trajectory. This work promises to have a tremendous impact on the field of walking recovery post-stroke and for other populations at risk for trip related falls. In particular, successful completion of this project will establish a paradigm shift from reactive fall prevention to proactive trip and fall prevention.